Advanced trajectory modeling and traffic simulation capabilities require high fidelity aircraft performance models (APMs), which usually are generic representations of the nominal aircraft performance. A main need, both in the current and future Air Traffic Management (ATM) paradigms, is to have highly accurate aircraft trajectory predictions computed on-ground. The widely accepted models used by the ATM community are the Eurocontrol BADA (Base of Aircraft Data) models.
BADA enables aircraft trajectory modeling in support of, among others, the following applications: (1) Air traffic simulation for operations analysis and Air Traffic Control (ATC) training; (2) Research and validation of new ATM concepts, tools and equipment before they are introduced into operational service; (3) Trajectory prediction for ground-based ATM systems (e.g., Flight Data Processing Systems); (4) Environmental assessment of air traffic operations (e.g., impact of noise and emissions).
BADA is made up of two main components: the model specification and the datasets. The model specification consists of a set of polynomial expressions used to calculate aircraft performance parameters, such as the drag coefficient, fuel consumption, engine thrust, etc. The polynomials are parameterized by a set of coefficients that particularize the polynomial expressions for specific aircraft types. These coefficients are the BADA datasets. Each aircraft type (airframe-engine combination) has associated therewith a specific BADA dataset. The BADA dataset of an aircraft type used in conjunction with the BADA model specification provide approximate values of the aircraft performance characteristics (e.g., drag polar, thrust coefficient, fuel consumption, etc.) of that aircraft.
However, these models lack information about aircraft performance degradation and, therefore, should be considered as generic representations of the average nominal aircraft performance of the aircraft based on manufacturers' information. Hence, potential improvements of the aircraft performance models are possible if data from real operations are available to the ground-based infrastructure. Taking advantage of the BADA model specifications, it would be possible to improve the datasets by updating the datasets using operational data of aircraft of the same type operating in a given environment by applying the proposed methodology.
The most accurate and up-to-date information about real aircraft performance is only available onboard. The Flight Management System (FMS) makes use of this information when commanding and controlling the aircraft. However, this information is not known by the Decision Support Tools (DST) supporting the standard operations. This information can only be accessed off-board by the airlines during the maintenance procedures (download of recorded flight data and health monitoring information).
Currently, there are no alternatives to using generic nominal APMs, such as BADA models, for representing the performance of the whole fleet of same-type aircraft. Those models have been widely accepted by the ATM community as the best representation of the performance of the majority of commercial aircrafts.
Further to the degradation suffered by the aircraft, document “Aircraft Performance Degradation” (M. Foueri 16th Performance and Operations Conference, May 2009) describes the influence of the engine degradation and the aerodynamic degradation over the optimal operational values of the aircraft performance. The degradation of aircraft performance admissible by the users is of course not unlimited. An example of how engine and aerodynamic degradation increase the fuel consumption and therefore decrease the efficiency of an aircraft is disclosed in document “Guidance Material and Best Practices for Fuel and Environmental Management” (International Air Transport Association (IATA), 3rd Edition, 2008). Airlines establish a detailed maintenance program for each individual aircraft in order to maintain its performance as closest as possible to the optimal operational values. When performance decreases below certain threshold, the maintenance procedures define how to proceed for recovering the optimal behavior. For example, for every 3,000 hours of flight time or 1,000 cycles, new airplanes lose about 1% of efficiency and after a few years of operation, the fuel burn tends to stabilize at 5% to 7% above the new aircraft performance levels. The extra fuel consumption can, therefore, be imputed to an increase of drag values and a decrease of thrust provided by the engine at same regimes, although more sophisticated approaches could also be valid.